Man-machine interaction face intelligent analysis system for internet games

Abstract

The invention relates to a man-machine interaction face intelligent analysis system for internet games. The man-machine interaction face intelligent analysis system comprises a data acquisition device, a successive learning device and an intelligent analysis mechanism. According to the method, the accuracy and the reliability of man-machine interaction user semantic expression for internet games can be improved.

Description

Technical Field

[0001] The present invention relates to the field of human-computer interaction, and in particular to a human-computer interaction facial intelligent analysis system for Internet games. Background Art

[0002] Human-Computer Interaction (HCI) refers to the process of information exchange between people and computers using a certain dialogue language and a certain interactive method to complete a certain task. Many well-known companies and academic institutions are studying human-computer interaction. In the history of computer development, people rarely paid attention to the usability of computers. Now, many computer users complain that computer manufacturers do not put enough effort into making their products "user-friendly". In turn, these computer system developers are also complaining, and their reason is: designing and manufacturing computers is a very complicated task. Just studying how to apply computers in new fields has occupied most of their energy, and there is really no extra energy to study how to improve the usability of computers.

[0003] An important application of human-computer interaction is for Internet games. For example, CN118121933A discloses a human-computer interaction method and system for Internet games, including: obtaining game information of a target user, forming analysis data according to the game information of the target user, forming determination data according to the game information and the analysis data of the target user, and adjusting the current game interface of the target user according to the determination data. The human-computer interaction method and system for Internet games judge and adjust the interface layout of the target game of the user on the current device according to the historical data of the user on the target game or the historical data of the user on the same type of games, and adjust the interface layout and display of the game interface according to the user's gaming habits, so that the user does not need to actively adjust the interface layout and display of the game, so that the user can enter the game state as soon as possible, thereby improving the user's gaming experience.

[0004] CN116999796A discloses a human-computer interaction control method, device, electronic device and storage medium for a game, and relates to the field of game technology. The method comprises: in response to a user sending a first game control signal by triggering a first button of the game control device, controlling a virtual game object to perform a first game behavior through a virtual auxiliary object; in response to the first game behavior, updating a first parameter of the virtual auxiliary object, the first parameter being used to set the number and / or duration of the first game behavior that the virtual auxiliary object can perform; in response to the first parameter satisfying a first parameter condition, sending a device parameter adjustment signal to the game control device to adjust the interaction parameter of the first button so that the pressing resistance of the first button is increased and / or the key travel is shortened. This technology can intuitively provide feedback information of the first button and improve the realism of the game.

[0005] CN112040327A discloses a human-computer interaction method, system, TV and storage medium for a TV game, the method comprising: obtaining a human action image; performing action recognition on the human action image according to a preset action library to obtain an action recognition result; obtaining a game instruction corresponding to the action recognition result, and operating a virtual character in the TV game according to the game instruction corresponding to the action recognition result. Among them, by collecting human action images and performing action recognition on the human action images, the human action is matched with the operation of the virtual character in the TV game, and then the virtual character is controlled, so that the game interaction with the TV is completed through human action, and there is no need to purchase supporting equipment, thereby improving the utilization rate of the game in the TV. Summary of the invention

[0006] In order to solve the technical problems in the prior art, the present invention provides a human-computer interaction facial intelligent analysis system for Internet games, which uses an artificial intelligence model to intelligently analyze the character string corresponding to the semantics to be expressed within a set time interval based on the user's facial visual characteristics and eye visual characteristics, thereby providing more expression channels for the user's semantic expression, wherein the structure of the artificial intelligence model used is customized, specifically, the artificial intelligence model is a BP neural network after completing each learning operation, and the number of learning operations of the BP neural network is positively correlated with the resolution of a data acquisition device that performs visual data acquisition, and also analyzes one or more human facial imaging areas in the received instant acquisition picture based on the corresponding appearance imaging characteristics of the human face, and outputs the human facial imaging area with the largest area among the one or more human facial imaging areas as a reference imaging area, and analyzes the area occupied by the left eye from the received reference imaging area based on the corresponding appearance imaging characteristics of the human eye. The imaging area occupied by the right eye and the imaging area occupied by the right eye are output as the first imaging area and the second imaging area respectively, and the number of pixels occupied by the reference imaging area corresponding to each acquisition moment, the depth of field value and coordinate value of each edge pixel point of the reference imaging area, the depth of field value and coordinate value of each edge pixel point of the first imaging area, and the depth of field value and coordinate value of each edge pixel point of the second imaging area are used as multiple visualization information corresponding to each acquisition moment, so as to selectively screen the multiple visualization information corresponding to each acquisition moment associated with the user's facial visual characteristics and eye visual characteristics for intelligent analysis as basic data for intelligent analysis, and adopt a BP neural network model to intelligently analyze the character string corresponding to the semantics of the user's facial expression expression within the set time interval according to the multiple visualization information of each acquisition moment evenly spaced within the set time interval and the duration length of the set time interval, and the character string corresponding to the semantics of the user's facial expression expression within the set time interval is a binary numerical representation of an ASCII code.

[0007] According to the present invention, a human-computer interaction facial intelligent analysis system for Internet games is provided, the system comprising: A data acquisition device, used to perform image data acquisition on the user's face to obtain and output an instant acquisition picture corresponding to each acquisition moment; A successive learning device, used for performing each learning operation on the BP neural network to obtain the BP neural network after each learning operation and output it as a BP neural network model, wherein the number of learning operations of the BP neural network is positively correlated with the resolution of the data acquisition device; A first analyzing device, connected to the data acquisition device, is used to analyze one or more human face imaging areas in the received real-time acquisition picture based on the appearance imaging characteristics corresponding to the human face, and output the human face imaging area with the largest area among the one or more human face imaging areas as a reference imaging area; A second analyzing device, connected to the first analyzing device, is used to analyze the imaging area occupied by the left eye and the imaging area occupied by the right eye from the received reference imaging area based on the appearance imaging characteristics corresponding to the human eye, and output them as the first imaging area and the second imaging area respectively; an intelligent analysis mechanism, connected to the successive learning device, the first analysis device and the second analysis device respectively, for using the number of pixels occupied by the reference imaging area corresponding to each acquisition moment, the depth of field value and coordinate value of each edge pixel of the reference imaging area, the depth of field value and coordinate value of each edge pixel of the first imaging area and the depth of field value and coordinate value of each edge pixel of the second imaging area as multiple pieces of visualization information corresponding to each acquisition moment, and using a BP neural network model to intelligently analyze the character string corresponding to the semantics of the user's facial expression expression within the set time interval according to the multiple pieces of visualization information of each acquisition moment evenly spaced within the set time interval and the duration length of the set time interval; Among them, using the BP neural network model to intelligently analyze the character string corresponding to the semantics of the user's facial expression expression in the set time interval according to the multiple visual information of each collection moment evenly spaced in the set time interval and the duration length of the set time interval includes: performing binary value conversion on the multiple visual information of each collection moment evenly spaced in the set time interval and the duration length of the set time interval respectively and then inputting them into the BP neural network model; Among them, using the BP neural network model to intelligently analyze the character string corresponding to the semantics of the user's facial expression expression within the set time interval based on multiple visualization information of each collection moment evenly spaced within the set time interval and the duration length of the set time interval also includes: running the BP neural network model to obtain the character string corresponding to the semantics of the user's facial expression expression within the set time interval output by the BP neural network model, and the character string corresponding to the semantics of the user's facial expression expression within the set time interval is a binary numerical representation of an ASCII code.

[0008] It can be seen that the present invention has at least the following four key inventive concepts: Invention concept 1: Using an artificial intelligence model to intelligently analyze the character string corresponding to the semantics to be expressed within a set time interval based on the user's facial visual characteristics and eye visual characteristics, thereby providing more expression channels for the user's semantic expression; Inventive concept 2: The structure customization of the artificial intelligence model used is specifically that the artificial intelligence model is a BP neural network after completing each learning operation, and the number of learning operations of the BP neural network is positively correlated with the resolution of the data acquisition device that performs visual data acquisition; Inventive concept three: based on the appearance imaging characteristics corresponding to the human face, one or more human face imaging areas in the received instant acquisition picture are parsed, and the human face imaging area with the largest area among the one or more human face imaging areas is output as a reference imaging area; based on the appearance imaging characteristics corresponding to the human eye, the imaging area occupied by the left eye and the imaging area occupied by the right eye are parsed from the received reference imaging area and output as the first imaging area and the second imaging area respectively; the number of pixels occupied by the reference imaging area corresponding to each acquisition moment, the depth of field value and coordinate value of each edge pixel point of the reference imaging area, the depth of field value and coordinate value of each edge pixel point of the first imaging area, and the depth of field value and coordinate value of each edge pixel point of the second imaging area are used as multiple pieces of visualization information corresponding to each acquisition moment, so as to selectively select multiple pieces of visualization information corresponding to each acquisition moment associated with the user's facial visual characteristics and eye visual characteristics for intelligent analysis as basic data for intelligent analysis; Invention concept four: Using a BP neural network model to intelligently analyze the character string corresponding to the semantics of the user's facial expression expression within a set time interval based on multiple visualization information of each collection moment evenly spaced within a set time interval and the duration length of the set time interval, the character string corresponding to the semantics of the user's facial expression expression within the set time interval is a binary numerical representation of an ASCII code. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The embodiments of the present invention will be described below with reference to the accompanying drawings, wherein: Figure 1 The figure is a structural block diagram of a human-computer interaction facial intelligent analysis system for Internet games according to implementation scheme A of the present invention.

[0010] Figure 2 The structure block diagram of the human-computer interaction facial intelligent analysis system for Internet games according to Embodiment B of the present invention is shown.

[0011] Figure 3 The structure block diagram of the human-computer interaction facial intelligent analysis system for Internet games according to the C implementation scheme of the present invention is shown. DETAILED DESCRIPTION

[0012] In the prior art, in order to enhance the effect of human-computer interaction, the user's face is often recognized. Although there is a way to judge the semantics that the human body wants to input based on the facial muscles of the human body, the above judgment mechanism is relatively rough, and the judgment result deviates greatly from the semantics that the human body wants to input. The main reason is that a reliable artificial intelligence model has not been established, and sufficient and comprehensive basic data has not been successfully screened.

[0013] The implementation scheme of the human-computer interaction facial intelligent analysis system for Internet games of the present invention will be described in detail below with reference to the accompanying drawings.

[0014] Figure 1 A structural block diagram of a human-computer interaction facial intelligent analysis system for Internet games according to embodiment A of the present invention, the system comprising: A data acquisition device, used to perform image data acquisition on the user's face to obtain and output an instant acquisition picture corresponding to each acquisition moment; Specifically, the data acquisition device is used to perform image data acquisition on the user's face to obtain and output the instant acquisition picture corresponding to each acquisition moment, including: the data acquisition device has a built-in on-site timing unit, an acquisition execution unit and a microcontroller chip; A successive learning device, used for performing each learning operation on the BP neural network to obtain the BP neural network after each learning operation and output it as a BP neural network model, wherein the number of learning operations of the BP neural network is positively correlated with the resolution of the data acquisition device; A first analyzing device, connected to the data acquisition device, is used to analyze one or more human face imaging areas in the received real-time acquisition picture based on the appearance imaging characteristics corresponding to the human face, and output the human face imaging area with the largest area among the one or more human face imaging areas as a reference imaging area; A second analyzing device, connected to the first analyzing device, is used to analyze the imaging area occupied by the left eye and the imaging area occupied by the right eye from the received reference imaging area based on the appearance imaging characteristics corresponding to the human eye, and output them as the first imaging area and the second imaging area respectively; an intelligent analysis mechanism, connected to the successive learning device, the first analysis device and the second analysis device respectively, for using the number of pixels occupied by the reference imaging area corresponding to each acquisition moment, the depth of field value and coordinate value of each edge pixel of the reference imaging area, the depth of field value and coordinate value of each edge pixel of the first imaging area and the depth of field value and coordinate value of each edge pixel of the second imaging area as multiple pieces of visualization information corresponding to each acquisition moment, and using a BP neural network model to intelligently analyze the character string corresponding to the semantics of the user's facial expression expression within the set time interval according to the multiple pieces of visualization information of each acquisition moment evenly spaced within the set time interval and the duration length of the set time interval; Among them, using the BP neural network model to intelligently analyze the character string corresponding to the semantics of the user's facial expression expression in the set time interval according to the multiple visual information of each collection moment evenly spaced in the set time interval and the duration length of the set time interval includes: performing binary value conversion on the multiple visual information of each collection moment evenly spaced in the set time interval and the duration length of the set time interval respectively and then inputting them into the BP neural network model; Among them, using the BP neural network model to intelligently analyze the character string corresponding to the semantics of the user's facial expression expression in the set time interval according to multiple visual information of each collection moment at uniform intervals in the set time interval and the duration length of the set time interval also includes: running the BP neural network model to obtain the character string corresponding to the semantics of the user's facial expression expression in the set time interval output by the BP neural network model, the character string corresponding to the semantics of the user's facial expression expression in the set time interval is a binary numerical representation of an ASCII code; And wherein, the successive learning device is used to perform each learning operation on the BP neural network to obtain the BP neural network after completing each learning operation and output it as a BP neural network model, and the number of learning operations of the BP neural network is positively correlated with the resolution of the data acquisition device, including: using a numerical conversion function to represent the numerical conversion relationship between the number of learning operations of the BP neural network and the resolution of the data acquisition device.

[0015] Figure 2 The structure block diagram of the human-computer interaction facial intelligent analysis system for Internet games according to Embodiment B of the present invention is shown.

[0016] Compared with the A embodiment, the human-computer interaction facial intelligent analysis system for Internet games shown in the B embodiment of the present invention may also include: a multi-directional service mechanism, which is arranged near the intelligent analysis mechanism, the sequential learning device, the first analysis device and the second analysis device and is respectively connected to the intelligent analysis mechanism, the sequential learning device, the first analysis device and the second analysis device; Among them, the multi-directional service mechanism is arranged near the intelligent analysis mechanism, the successive learning device, the first analysis device and the second analysis device and is respectively connected to the intelligent analysis mechanism, the successive learning device, the first analysis device and the second analysis device, including: the multi-directional service mechanism is used to provide the intelligent analysis mechanism, the successive learning device, the first analysis device and the second analysis device with the required operating current respectively.

[0017] Figure 3 The structure block diagram of the human-computer interaction facial intelligent analysis system for Internet games according to the C implementation scheme of the present invention is shown.

[0018] Compared with the A embodiment, the human-computer interaction facial intelligent analysis system for Internet games shown in the C embodiment of the present invention may also include: an oscillation actuator, disposed near the intelligent analysis mechanism, the successive learning device, the first analysis device, and the second analysis device, and connected to the intelligent analysis mechanism, the successive learning device, the first analysis device, and the second analysis device, respectively; Among them, the oscillation actuator is arranged near the intelligent analysis mechanism, the successive learning device, the first analysis device and the second analysis device and is respectively connected to the intelligent analysis mechanism, the successive learning device, the first analysis device and the second analysis device, including: the oscillation actuator is used to provide the intelligent analysis mechanism, the successive learning device, the first analysis device and the second analysis device with the reference waveform signals they need respectively.

[0019] Next, the specific structure of the human-computer interaction facial intelligent analysis system for Internet games of the present invention will be further described.

[0020] In the human-computer interaction facial intelligent analysis system for Internet games according to various embodiments of the present invention: A programmable logic device is used to perform image data processing on the output data of the intelligent analysis mechanism, the successive learning device, the first analysis device and the second analysis device to obtain output processing data corresponding to the intelligent analysis mechanism, the successive learning device, the first analysis device and the second analysis device respectively.

[0021] In the human-computer interaction facial intelligent analysis system for Internet games according to various embodiments of the present invention: Using a programmable logic device to perform image data processing on the output data of the intelligent analysis mechanism, the successive learning device, the first analysis device and the second analysis device to obtain the output processing data corresponding to the intelligent analysis mechanism, the successive learning device, the first analysis device and the second analysis device respectively includes: performing image sharpening processing on the output data of the intelligent analysis mechanism, the successive learning device, the first analysis device and the second analysis device to obtain the output processing data corresponding to the intelligent analysis mechanism, the successive learning device, the first analysis device and the second analysis device respectively.

[0022] In the human-computer interaction facial intelligent analysis system for Internet games according to various embodiments of the present invention: Using a programmable logic device to perform image data processing on the output data of the intelligent analysis mechanism, the successive learning device, the first analysis device and the second analysis device to obtain the output processing data corresponding to the intelligent analysis mechanism, the successive learning device, the first analysis device and the second analysis device respectively includes: performing image enhancement processing on the output data of the intelligent analysis mechanism, the successive learning device, the first analysis device and the second analysis device to obtain the output processing data corresponding to the intelligent analysis mechanism, the successive learning device, the first analysis device and the second analysis device respectively.

[0023] In the human-computer interaction facial intelligent analysis system for Internet games according to various embodiments of the present invention: Using a programmable logic device to perform image data processing on the output data of the intelligent analysis mechanism, the successive learning device, the first analysis device and the second analysis device to obtain the output processing data corresponding to the intelligent analysis mechanism, the successive learning device, the first analysis device and the second analysis device respectively includes: performing image filtering processing on the output data of the intelligent analysis mechanism, the successive learning device, the first analysis device and the second analysis device to obtain the output processing data corresponding to the intelligent analysis mechanism, the successive learning device, the first analysis device and the second analysis device respectively.

[0024] And in the human-computer interaction facial intelligent analysis system for Internet games according to various embodiments of the present invention: Using a programmable logic device to perform image data processing on the output data of the intelligent analysis mechanism, the successive learning device, the first analysis device and the second analysis device to obtain the output processing data corresponding to the intelligent analysis mechanism, the successive learning device, the first analysis device and the second analysis device respectively includes: performing distortion correction processing on the output data of the intelligent analysis mechanism, the successive learning device, the first analysis device and the second analysis device to obtain the output processing data corresponding to the intelligent analysis mechanism, the successive learning device, the first analysis device and the second analysis device respectively.

[0025] In addition, in the human-computer interactive facial intelligent analysis system for Internet games, a numerical conversion function is used to represent the numerical conversion relationship in which the number of learning operations of the BP neural network is positively correlated with the resolution of the data acquisition device, including: in the numerical conversion function, the resolution of the data acquisition device is the input value of the numerical conversion function, and the number of learning operations of the BP neural network corresponding to the resolution of the data acquisition device is the output value of the numerical conversion function.

[0026] The human-computer interaction facial intelligent analysis system for Internet games of the present invention aims to solve the technical problem of insufficient accuracy and reliability of human-computer interaction systems based on the semantic recognition mechanism of facial expressions in the prior art. By adopting an artificial intelligence model to intelligently analyze the character string corresponding to the semantics to be expressed within a set time interval based on the user's facial visual characteristics and eye visual characteristics, more expression channels are provided for the user's semantic expression, the accuracy and reliability of the user's semantic expression are improved, and the above-mentioned technical problems are solved.

[0027] The present invention has been described with reference to its typical embodiments, but those skilled in the art will be able to make various modifications to the described embodiments without departing from its purpose and scope. The terms and descriptions used herein are set forth by way of example only and are not intended to be limiting. In particular, although the method has been described by way of example, the steps of the method may be performed in a different order than that illustrated or simultaneously. Those skilled in the art will recognize that these and other changes may be made within the purpose and scope defined by the following claims and their equivalents.

Claims

1. A human-computer interaction facial intelligent analysis system for Internet games, characterized in that: The system comprises: A data acquisition device, used to perform image data acquisition on the user's face to obtain and output an instant acquisition picture corresponding to each acquisition moment; A successive learning device, used for performing each learning operation on the BP neural network to obtain the BP neural network after each learning operation and output it as a BP neural network model, wherein the number of learning operations of the BP neural network is positively correlated with the resolution of the data acquisition device; A first analyzing device, connected to the data acquisition device, is used to analyze one or more human face imaging areas in the received real-time acquisition picture based on the appearance imaging characteristics corresponding to the human face, and output the human face imaging area with the largest area among the one or more human face imaging areas as a reference imaging area; A second analyzing device, connected to the first analyzing device, is used to analyze the imaging area occupied by the left eye and the imaging area occupied by the right eye from the received reference imaging area based on the appearance imaging characteristics corresponding to the human eye, and output them as the first imaging area and the second imaging area respectively; an intelligent analysis mechanism, connected to the successive learning device, the first analysis device and the second analysis device respectively, for using the number of pixels occupied by the reference imaging area corresponding to each acquisition moment, the depth of field value and coordinate value of each edge pixel of the reference imaging area, the depth of field value and coordinate value of each edge pixel of the first imaging area and the depth of field value and coordinate value of each edge pixel of the second imaging area as multiple pieces of visualization information corresponding to each acquisition moment, and using a BP neural network model to intelligently analyze the character string corresponding to the semantics of the user's facial expression expression within the set time interval according to the multiple pieces of visualization information of each acquisition moment evenly spaced within the set time interval and the duration length of the set time interval; Among them, using the BP neural network model to intelligently analyze the character string corresponding to the semantics of the user's facial expression expression in the set time interval according to the multiple visual information of each collection moment evenly spaced in the set time interval and the duration length of the set time interval includes: performing binary value conversion on the multiple visual information of each collection moment evenly spaced in the set time interval and the duration length of the set time interval respectively and then inputting them into the BP neural network model; Among them, using the BP neural network model to intelligently analyze the character string corresponding to the semantics of the user's facial expression expression within the set time interval based on multiple visualization information of each collection moment evenly spaced within the set time interval and the duration length of the set time interval also includes: running the BP neural network model to obtain the character string corresponding to the semantics of the user's facial expression expression within the set time interval output by the BP neural network model, and the character string corresponding to the semantics of the user's facial expression expression within the set time interval is a binary numerical representation of an ASCII code.

2. The human-computer interaction facial intelligent analysis system for Internet games as claimed in claim 1, characterized in that: A successive learning device is used to perform each learning operation on the BP neural network to obtain the BP neural network after completing each learning operation and output it as a BP neural network model. The number of learning operations of the BP neural network is positively correlated with the resolution of the data acquisition device, including: using a numerical conversion function to represent the numerical conversion relationship between the number of learning operations of the BP neural network and the resolution of the data acquisition device.

3. The human-computer interaction facial intelligent analysis system for Internet games as claimed in claim 2, characterized in that: The system further comprises: a multi-directional service mechanism, which is arranged near the intelligent analysis mechanism, the sequential learning device, the first analysis device and the second analysis device and is respectively connected to the intelligent analysis mechanism, the sequential learning device, the first analysis device and the second analysis device; Among them, the multi-directional service mechanism is arranged near the intelligent analysis mechanism, the successive learning device, the first analysis device and the second analysis device and is respectively connected to the intelligent analysis mechanism, the successive learning device, the first analysis device and the second analysis device, including: the multi-directional service mechanism is used to provide the intelligent analysis mechanism, the successive learning device, the first analysis device and the second analysis device with the required operating current respectively.

4. The human-computer interaction facial intelligent analysis system for Internet games as claimed in claim 2, characterized in that: The system further comprises: an oscillation actuator, disposed near the intelligent analysis mechanism, the successive learning device, the first analysis device, and the second analysis device, and connected to the intelligent analysis mechanism, the successive learning device, the first analysis device, and the second analysis device, respectively; Among them, the oscillation actuator is arranged near the intelligent analysis mechanism, the successive learning device, the first analysis device and the second analysis device and is respectively connected to the intelligent analysis mechanism, the successive learning device, the first analysis device and the second analysis device, including: the oscillation actuator is used to provide the intelligent analysis mechanism, the successive learning device, the first analysis device and the second analysis device with the reference waveform signals they need respectively.

5. The human-computer interaction facial intelligent analysis system for Internet games as described in any one of claims 2 to 4, characterized in that: A programmable logic device is used to perform image data processing on the output data of the intelligent analysis mechanism, the successive learning device, the first analysis device and the second analysis device to obtain output processing data corresponding to the intelligent analysis mechanism, the successive learning device, the first analysis device and the second analysis device respectively.

6. The human-computer interaction facial intelligent analysis system for Internet games as claimed in claim 5, characterized in that: Using a programmable logic device to perform image data processing on the output data of the intelligent analysis mechanism, the successive learning device, the first analysis device and the second analysis device to obtain the output processing data corresponding to the intelligent analysis mechanism, the successive learning device, the first analysis device and the second analysis device respectively includes: performing image sharpening processing on the output data of the intelligent analysis mechanism, the successive learning device, the first analysis device and the second analysis device to obtain the output processing data corresponding to the intelligent analysis mechanism, the successive learning device, the first analysis device and the second analysis device respectively.

7. The human-computer interaction facial intelligent analysis system for Internet games as claimed in claim 5, characterized in that: Using a programmable logic device to perform image data processing on the output data of the intelligent analysis mechanism, the successive learning device, the first analysis device and the second analysis device to obtain the output processing data corresponding to the intelligent analysis mechanism, the successive learning device, the first analysis device and the second analysis device respectively includes: performing image enhancement processing on the output data of the intelligent analysis mechanism, the successive learning device, the first analysis device and the second analysis device to obtain the output processing data corresponding to the intelligent analysis mechanism, the successive learning device, the first analysis device and the second analysis device respectively.

8. The human-computer interaction facial intelligent analysis system for Internet games as claimed in claim 5, characterized in that: Using a programmable logic device to perform image data processing on the output data of the intelligent analysis mechanism, the successive learning device, the first analysis device and the second analysis device to obtain the output processing data corresponding to the intelligent analysis mechanism, the successive learning device, the first analysis device and the second analysis device respectively includes: performing image filtering processing on the output data of the intelligent analysis mechanism, the successive learning device, the first analysis device and the second analysis device to obtain the output processing data corresponding to the intelligent analysis mechanism, the successive learning device, the first analysis device and the second analysis device respectively.

9. The human-computer interaction facial intelligent analysis system for Internet games as claimed in claim 5, characterized in that: Using a programmable logic device to perform image data processing on the output data of the intelligent analysis mechanism, the successive learning device, the first analysis device and the second analysis device to obtain the output processing data corresponding to the intelligent analysis mechanism, the successive learning device, the first analysis device and the second analysis device respectively includes: performing distortion correction processing on the output data of the intelligent analysis mechanism, the successive learning device, the first analysis device and the second analysis device to obtain the output processing data corresponding to the intelligent analysis mechanism, the successive learning device, the first analysis device and the second analysis device respectively.

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